import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from Perceptron import plot_decision_regions


class AdalineSGD(object):
    """ADAptive LInear NEuron classifier.

    Parameters
    ------------
    eta : float
        Learning rate (between 0.0 and 1.0)
    n_iter : int
        Passes over the training dataset.

    Attributes
    -----------
    w_ : 1d-array
        Weights after fitting.
    cost_ : list
        Sum-of-squares cost function value averaged over all
        training samples in each epoch.
    shuffle : bool (default: True)
        Shuffles training data every epoch if True to prevent cycles.
    random_state : int (default: None)
        Set random state for shuffling and initializing the weights.

    """
    def __init__(self, eta=0.01, n_iter=10, shuffle=True, random_state=None):
        self.eta = eta
        self.n_iter = n_iter
        self.w_initialized = False
        self.shuffle = shuffle
        if random_state:
            np.random.seed(random_state)

    def fit(self, X, y):
        """ Fit training data.

        Parameters
        ----------
        X : {array-like}, shape = [n_samples, n_features]
            Training vectors, where n_samples is the number of samples and
            n_features is the number of features.
        y : array-like, shape = [n_samples]
            Target values.

        Returns
        -------
        self : object

        """
        self._initialize_weights(X.shape[1])
        self.cost_ = []
        for i in range(self.n_iter):
            if self.shuffle:
                X, y = self._shuffle(X, y)
            cost = []
            for xi, target in zip(X, y):
                cost.append(self._update_weights(xi, target))
            avg_cost = sum(cost) / len(y)
            self.cost_.append(avg_cost)
        return self

    def partial_fit(self, X, y):
        """Fit training data without reinitializing the weights"""
        if not self.w_initialized:
            self._initialize_weights(X.shape[1])
        if y.ravel().shape[0] > 1:
            for xi, target in zip(X, y):
                self._update_weights(xi, target)
        else:
            self._update_weights(X, y)
        return self

    def _shuffle(self, X, y):
        """Shuffle training data"""
        r = np.random.permutation(len(y))
        return X[r], y[r]

    def _initialize_weights(self, m):
        """Initialize weights to zeros"""
        self.w_ = np.zeros(1 + m)
        self.w_initialized = True

    def _update_weights(self, xi, target):
        """Apply Adaline learning rule to update the weights"""
        output = self.net_input(xi)
        error = (target - output)
        self.w_[1:] += self.eta * xi.dot(error)
        self.w_[0] += self.eta * error
        cost = 0.5 * error**2
        return cost

    def net_input(self, X):
        """Calculate net input"""
        return np.dot(X, self.w_[1:]) + self.w_[0]

    def activation(self, X):
        """Compute linear activation"""
        return self.net_input(X)

    def predict(self, X):
        """Return class label after unit step"""
        return np.where(self.activation(X) >= 0.0, 1, -1)


df = pd.read_csv('https://archive.ics.uci.edu/ml/'
                 'machine-learning-databases/iris/iris.data', header=None)
# 这个CSV文件有5列，前4列是特征，最后一列是鸢尾花的品种（字符串类型）。由于没有表头，pandas会默认将列命名为0,1,2,3,4。
print(50 * '=')
print(df)
print("----------------------------")
# 查看数据集的最后5行
print(df.tail())

#############################################################################
print(50 * '=')
print('Plotting the Iris data')
print(50 * '-')

# select setosa and versicolor
# 选择前100个样本（Iris-setosa和Iris-versicolor）
""" 
# 提取特征矩阵 X（前100行的前4列）
X = df.iloc[0:100, [0, 1, 2, 3]].values

# 提取标签向量 y
y = df.iloc[0:100, 4].values

"""
y = df.iloc[0:100, 4].values
# # 创建二进制标签 (setosa: -1, versicolor: 1)
y = np.where(y == 'Iris-setosa', -1, 1)

# extract sepal length and petal length
"""
行索引 0:100：选取前100个样本（排除第三类 virginica）
列索引 [0, 2]：选择萼片长度（第0列）和花瓣长度（第2列"""
X = df.iloc[0:100, [0, 2]].values
X_std = np.copy(X)
X_std[:, 0] = (X[:, 0] - X[:, 0].mean()) / X[:, 0].std()
X_std[:, 1] = (X[:, 1] - X[:, 1].mean()) / X[:, 1].std()

ada = AdalineSGD(n_iter=15, eta=0.01, random_state=1)
ada.fit(X_std, y)

plot_decision_regions(X_std, y, classifier=ada)
plt.title('Adaline - Stochastic Gradient Descent')
plt.xlabel('sepal length [standardized]')
plt.ylabel('petal length [standardized]')
plt.legend(loc='upper left')

# plt.tight_layout()
# plt.savefig('./adaline_4.png', dpi=300)
plt.show()

plt.plot(range(1, len(ada.cost_) + 1), ada.cost_, marker='o')
plt.xlabel('Epochs')
plt.ylabel('Average Cost')

# plt.tight_layout()
# plt.savefig('./adaline_5.png', dpi=300)
plt.show()

ada = ada.partial_fit(X_std[0, :], y[0])
